Quantum network tomography of Rydberg arrays by machine learning

Abstract

Configurable arrays of optically trapped Rydberg atoms are a versatile platform for quantum computation and quantum simulation, also allowing controllable decoherence. We demonstrate theoretically, that they also enable proof-of-principle demonstrations for a technique to build models for open quantum dynamics by machine learning with artificial neural networks, recently proposed in [Mukherjee et al. [arXiv:2409.18822] (2024)]. Using the outcome of quantum transport through a network of sites that correspond to excited Rydberg atoms, the multi-stage neural network algorithm successfully identifies the number of atoms (or nodes in the network), and subsequently their location. It further extracts an effective interaction Hamiltonian and decoherence operators induced by the environment. To probe the Rydberg array, one initiates dynamics repeatedly from the same initial state and then measures the transport probability to an output atom. Large datasets are generated by varying the position of the latter. Measurements are required in only one single basis, making the approach complementary to e.g. quantum process tomography. The cold atom platform discussed in this article can be used to explore the performance of the proposed protocol when training the neural network with simulation data, but then applying it to construct models based on experimental data.